NCE method that estimates probability density with unnormalized modeling by means of a sampling strategy was presented in [1]}.
To be specific, given sample \(x\) , the method is designed to maximize the log-ratio between the estimated probability by a learned model, \(p_m(x)\) , and the probability derived from a noise distribution, \(p_n(x)\) , if \(x\)  is a true sample. It minimizes the ratio, otherwise.
In language processing domain, NCE has been applied to learn word embeddings from a large vocabulary by avoiding softmax computation [2]}.
